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Готові списки джерел за темами / Classification exoplanets

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Добірка наукової літератури з теми "Classification exoplanets"

Автор: Grafiati

Опубліковано: 28 травня 2022

Оновлено: 31 липня 2025

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Статті в журналах з теми "Classification exoplanets"

1

Jiang, Jonathan H., Philip E. Rosen, Christina X. Liu, Qianzhuang Wen, and Yanbei Chen. "Analysis of Habitability and Stellar Habitable Zones from Observed Exoplanets." Galaxies 12, no. 6 (2024): 86. https://doi.org/10.3390/galaxies12060086.

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The investigation of exoplanetary habitability is integral to advancing our knowledge of extraterrestrial life potential and detailing the environmental conditions of distant worlds. In this analysis, we explore the properties of exoplanets situated with respect to circumstellar habitable zones by implementing a sophisticated filtering methodology on data from the NASA Exoplanet Archive. This research encompasses a thorough examination of 5595 confirmed exoplanets listed in the Archive as of 10 March 2024, systematically evaluated according to their calculated average surface temperatures and

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2

Donnelly, Shane, and Ayan Dutta. "Kepler Light Curve Classification Using Deep Learning and Markov Transition Field (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23475–76. http://dx.doi.org/10.1609/aaai.v38i21.30435.

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An exoplanet is a planet, which is not a part of our solar system. Whether life exists in one or more of these exoplanets has fascinated humans for centuries. NASA’s Kepler Space Telescope has discovered more than 70% of known exoplanets in our universe. However, manually determining whether a Kepler light curve indicates an exoplanet or not becomes infeasible with the large volume of data. Due to this, we propose a deep learning-based strategy to automatically classify a Kepler light curve. More specifically, we first convert the light curve time series into its corresponding Markov Transitio

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3

Jin, Yucheng, Lanyi Yang, and Chia-En Chiang. "Exoplanets Identification and Clustering with Machine Learning Methods." Machine Learning and Applications: An International Journal 9, no. 1 (2022): 1–14. http://dx.doi.org/10.5121/mlaij.2022.9101.

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The discovery of habitable exoplanets has long been a heated topic in astronomy. Traditional methods for exoplanet identification include the wobble method, direct imaging, gravitational microlensing, etc., which not only require a considerable investment of manpower, time, and money, but also are limited by the performance of astronomical telescopes. In this study, we proposed the idea of using machine learning methods to identify exoplanets. We used the Kepler dataset collected by NASA from the Kepler Space Observatory to conduct supervised learning, which predicts the existence of exoplanet

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4

Odrzywolek, A., and J. Rafelski. "Classification of Exoplanets According to Density." Acta Physica Polonica B 49, no. 11 (2018): 1917. http://dx.doi.org/10.5506/aphyspolb.49.1917.

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5

Sarkar, Jyotirmoy, Kartik Bhatia, Snehanshu Saha, Margarita Safonova, and Santonu Sarkar. "Postulating exoplanetary habitability via a novel anomaly detection method." Monthly Notices of the Royal Astronomical Society 510, no. 4 (2021): 6022–32. http://dx.doi.org/10.1093/mnras/stab3556.

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ABSTRACT A profound shift in the study of cosmology came with the discovery of thousands of exoplanets and the possibility of the existence of billions of them in our Galaxy. The biggest goal in these searches is whether there are other life-harbouring planets. However, the question which of these detected planets are habitable, potentially-habitable, or maybe even inhabited, is still not answered. Some potentially habitable exoplanets have been hypothesised, but since Earth is the only known habitable planet, measures of habitability are necessarily determined with Earth as the reference. Sev

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6

Sasmita Kumari Nayak. "Classification of Kepler exoplanet searching from galaxy using machine learning model." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 227–30. http://dx.doi.org/10.30574/wjarr.2024.21.1.0012.

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The Kepler exoplanet mission was designed specifically to search the Milky Way galaxy for numerous small planets the size of Earth that are either in or close to the habitable zone. An exoplanet is a planet that orbits stars outside of our solar system. The dataset, which was gathered from the Kaggle website, has 50 columns and 9564 samples. The target variable in the dataset, KOI-disposition, comprises candidate, false positive and confirmed data. We discovered that 5000 samples out of 9564 samples were false positives; each confirmed sample had 2282 candidates. While there are many stars and

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7

Sasmita, Kumari Nayak. "Classification of Kepler exoplanet searching from galaxy using machine learning model." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 227–30. https://doi.org/10.5281/zenodo.13142011.

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Анотація:

The Kepler exoplanet mission was designed specifically to search the Milky Way galaxy for numerous small planets the size of Earth that are either in or close to the habitable zone. An exoplanet is a planet that orbits stars outside of our solar system. The dataset, which was gathered from the Kaggle website, has 50 columns and 9564 samples. The target variable in the dataset, KOI-disposition, comprises candidate, false positive and confirmed data. We discovered that 5000 samples out of 9564 samples were false positives; each confirmed sample had 2282 candidates. While there are many stars and

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8

Mondal, S. "Classification of Hot Jupiter Population through Statistical Framework." Journal of Scientific Research 14, no. 2 (2022): 513–19. http://dx.doi.org/10.3329/jsr.v14i2.56497.

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The recent trend in the discovery of a range of exoplanets opens up a door to evaluate their origin and classification under the light of different planetary attributes. This paper enthusiastically focused on a typical branch of exoplanets, hot Jupiter, and several planetary characteristics were observed to frame the population into substantive categories. In this paper, a statistical framework was also established to understand different planetary formation processes for hot Jupiters. Finally, the relevance of hot Jupiters in search of habitable planets is also discussed briefly.

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9

Margot, Jean-Luc, Brett Gladman, and Tony Yang. "Quantitative Criteria for Defining Planets." Planetary Science Journal 5, no. 7 (2024): 159. http://dx.doi.org/10.3847/psj/ad55f3.

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Abstract The current International Astronomical Union (IAU) definition of “planet” is problematic because it is vague and excludes exoplanets. Here, we describe aspects of quantitative planetary taxonomy and examine the results of unsupervised clustering of solar system bodies to guide the development of possible classification frameworks. Two unsurprising conclusions emerged from the clustering analysis: (1) satellites are distinct from planets and (2) dynamical dominance is a natural organizing principle for planetary taxonomy. To generalize an existing dynamical dominance criterion, we adop

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10

Hasegawa, Yasuhiro, and Mark R. Swain. "Bulk and Atmospheric Metallicities as Direct Probes of Sequentially Varying Accretion Mechanisms of Gas and Solids Onto Planets." Astrophysical Journal Letters 973, no. 2 (2024): L46. http://dx.doi.org/10.3847/2041-8213/ad7957.

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Abstract Core accretion is the standard scenario of planet formation, wherein planets are formed by sequential accretion of gas and solids, and is widely used to interpret exoplanet observations. However, no direct probes of the scenario have been discussed yet. Here, we introduce an onion-like model as one idealization of sequential accretion and propose that bulk and atmospheric metallicities of exoplanets can be used as direct probes of the process. Our analytical calculations, coupled with observational data, demonstrate that the trend of observed exoplanets supports the sequential accreti

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Більше джерел

Дисертації з теми "Classification exoplanets"

1

Gibson, Neale Patrick. "High precision transit photometry for the detection and classification of exoplanets." Thesis, Queen's University Belfast, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527702.

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2

Вівдіч, І. К. "Екзопланети". Thesis, Сумський державний університет, 2015. http://essuir.sumdu.edu.ua/handle/123456789/44012.

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Світ не стоїть на місці, а людська думка немає кордонів. Тому як тільки з’явилися ідеї стосовного то, що зірки в небі – це віддалені об’єкти, як наше Сонце, відразу почали розмірковувати про те, що є вірогідність існування планет навколо них та життя на цих тілах.

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3

Hardegree-Ullman, Kevin Karlyle. "Kepler Planet Occurrence Rates for Mid-Type M Dwarfs as a Function of Spectral Type." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1533214839813619.

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Частини книг з теми "Classification exoplanets"

1

Basak, Suryoday. "Thermal Suitability Scheme: Habitability Classification of Exoplanets." In Modeling, Machine Learning and Astronomy. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-6463-9_14.

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2

Patel, Yash. "Predicting Exoplanets Habitability: Metrics and Models." In Anomaly Detection - Methods, Complexities and Applications [Working Title]. IntechOpen, 2024. https://doi.org/10.5772/intechopen.1007686.

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Анотація:

The search for habitable exoplanets has become a critical focus in the field of astrobiology and planetary science, with technological advancements such as the Kepler and TESS missions enabling the discovery of thousands of exoplanets. However, determining whether these planets are habitable remains a significant challenge due to the complex factors that govern planetary habitability, including atmospheric composition, temperature, and distance from the host star. To address these challenges, several habitability metrics, such as the Earth Habitability Index (EHI) and the Cobb-Douglas Habitabi

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3

Bernath, Peter F. "Astronomical Spectroscopy." In Spectra of Atoms and Molecules, 5th ed. Oxford University PressNew York, 2025. https://doi.org/10.1093/oso/9780197754498.003.0012.

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Abstract The chapter begins with a discussion of star formation from dark interstellar clouds illustrated with radio telescope spectra. The classification of stars and brown dwarfs from their spectra is presented. The chapter concludes with spectra of planets and their moons, and exoplanets.

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Тези доповідей конференцій з теми "Classification exoplanets"

1

Priya, M. Yagnasri, Sanjivani P. Shendre, Samrudhi S. Kamble, Swetha Sridhar, and Sarada Jayan. "Statistical and Machine Learning Perspectives on Exoplanets Prediction and Classification." In 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC). IEEE, 2024. https://doi.org/10.1109/icicec62498.2024.10808686.

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2

G, Ishvarya, Kavya Sree Kammari, Sharanya Vanraj Thambi, and Manju Venugopalan. "Enhanced Classification of Exoplanets Using Machine Learning: Integrating Feature Selection, Ensemble Algorithms, and Explainable AI Insights." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725900.

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3

Tyagi, Deepanshu, Renu Chaudhary, Gurpreet Kaur, and Rekha Kumari. "Identification of Exoplanets using Machine Learning." In 2nd International Conference on Emerging Applications of Artificial Intelligence, Machine Learning and Cybersecurity. AIJR Publisher, 2025. https://doi.org/10.21467/proceedings.178.20.

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Over the past decade more than a million stars have been observed as part of efforts to identify transiting planets. Identifying potential exoplanet candidates requires manual interpretation which is both labor intensive and susceptible to human error the consequences of which are often difficult to quantify. This paper describes a novel approach that utilizes a neural network to automate the identification of potential exoplanet candidates in large planetary search programs a method that contrasts with traditional manual techniques. A combination of classification models and diverse datasets

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4

Öztürk, O., and A. Erdem. "On the classification of exoplanets according to Safronov number." In TURKISH PHYSICAL SOCIETY 33RD INTERNATIONAL PHYSICS CONGRESS (TPS33). Author(s), 2018. http://dx.doi.org/10.1063/1.5025961.

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5

Herur, Aahish Nagesh, Raquib Tajmohamed, and J. Godwin Ponsam. "Detection and classification of exoplanets using hybrid kNN model." In 2022 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2022. http://dx.doi.org/10.1109/iccci54379.2022.9741029.

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6

Zagorulia, D., and M. Lisakov. "Classification of jets inactive galactic nuclei." In Modern astronomy: from the Early Universe to exoplanets and black holes. Special Astrophysical Observatory of the Russian Academy of Sciences, 2024. https://doi.org/10.26119/vak2024.018.

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This work explores the application of machine learning techniques to classifying jetted active galactic nuclei (AGN) based on Very-Long-Baseline Interferometry (VLBI) observations at frequencies 1-90 GHz. Building upon previous work by Fanaroff and Riley, who classified relativistic jets in radio galaxies on kiloparsec scales, we extend this classification to parsec scales, closer to the central supermassive black hole. This approach enables detailed study of jet spatial structures and can help enhancing accuracy in global positioning systems. We define four morphological classes: single Gauss

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7

Feoktistova, E., Zh Rodionova, and V. Shevchenko. "Morphometric parameters of fresh lunar craters." In Modern astronomy: from the Early Universe to exoplanets and black holes. Special Astrophysical Observatory of the Russian Academy of Sciences, 2024. https://doi.org/10.26119/vak2024.101.

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We study the morphometric parameters of 111 lunar craters with a diameter of 10 km or more, the formation of which, according to some researchers, can be attributed to the Copernican period. These craters were selected by analyzing data from the Diviner spectrometer on board the LRO probe. The purpose of this work is to study the morphometric characteristics of such craters in order to identify their characteristic features. According to the results obtained, the morphometric parameters of the fresh lunar craters are significantly similar to the morphometric parameters of lunar craters of the

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8

Shamsutdinova, J., and L. Kashapova. "Recognition of solar flares in the microwave range of observations using machine learning." In Modern astronomy: from the Early Universe to exoplanets and black holes. Special Astrophysical Observatory of the Russian Academy of Sciences, 2024. https://doi.org/10.26119/vak2024.113.

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We present the results of testing machine learning methods for the recognition of solar flares observed in the microwave range. The catalogue of observations by the Nobeyama spectropolarimeters (NoRP) was used for both training and analysis of method effectiveness. The input data are one-dimensional temporal profiles with a frequency of 9.4 GHz, which are mostly associated with the emission of an optically thin source and the non-thermal emission from accelerated electrons. The input dataset consisted of 100 events and included temporal profiles with simple and complex structures. The aim of r

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9

Debosscher, J., L. M. Sarro, M. López, C. Aerts, and Coryn A. L. Bailer-Jones. "Methodology for Automated Supervised Classification of Light Curves in the CoRoT Exoplanet Database." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059056.

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